A pan-cancer analysis of the oncogenic and immunological roles of transglutaminase 1 (TGM1) in human cancer

Abstract

Background: There is currently a limited number of studies on transglutaminase type 1 (TGM1) in tumors. The objective of this study is to perform a comprehensive analysis across various types of cancer to determine the prognostic significance of TGM1 in tumors and investigate its role in the immune environment.

Method: Pan-cancer and mutational data were retrieved from the TCGA database and analyzed using R (version 3.6.4) and its associated software package. The expression difference and prognosis of TGM1 were examined, along with its correlation with tumor heterogeneity, stemness, mutation landscape, and RNA modification. Additionally, the relationship between TGM1 expression and tumor immunity was investigated using the TIMER method.

Results: TGM1 is expressed differently in various tumors and normal samples and is associated with the overall survival and progression-free time of KIRC, ACC, SKCM, LIHC, and STES. In LICH, we found a negative correlation between TGM1 expression and 6 indicators of tumor stemness. The mutation frequencies of BLCA, LIHC, and KIRC were 1.7%, 0.3%, and 0.3% respectively. In BLCA and BRCA, there was a significant correlation between TGM1 expression and the infiltration of CD4 + T cells, CD8 + T cells, neutrophils, and dendritic cells.

Conclusion: TGM1 has the potential to serve as both a prognostic marker and a drug target.

Keywords: Pan-cancer biomarker; Transglutaminase 1; Tumor-infiltrating cells.

Fig. 1

Differential expression and prognosis analysis of TGM1. A pan-cancer analysis of TGM1 for differential expression between tumor and normal tissues; B pan-cancer analysis of TGM1 for OS; C pan-cancer analysis of TGM1 for PFI; OS: overall survival; PFI: progression-free interval

Fig.2

The pan-cancer Spearman analysis of tumor heterogeneity and TGM1 expression. A the correlation between HRD and TGM1 level; B the correlation between LOH and TGM1 level; C the correlation between MATH and TGM1 level; D the correlation between MSI and TGM1 level; E the correlation between NEO and TGM1 level; (F) the correlation between tumor ploidy and TGM1 level; G the correlation between tumor purity and TGM1 level; H the correlation between TMB and TGM1 level. HRD: homologous recombination deficiency; LOH loss of heterozygosity, MATH: mutant-allele tumor heterogeneity, MSI microsatellite instability, NEO neoantigen, TMB tumor mutation burden

Fig.3

The pan-cancer Spearman analysis of tumor stemness and TGM1 expression. A The correlation between tumor stemness and TGM1 level using DMPss; B the correlation between tumor stemness and TGM1 level using DNAss; C the correlation between tumor stemness and TGM1 level using ENHss; D the correlation between tumor stemness and TGM1 level using EREG.EXPss; E the correlation between tumor stemness and TGM1 level using EREG-METHss; F the correlation between tumor stemness and TGM1 level using RNAss. DNAss DNA methylation based, DMPss differentially methylated probes-based, EHNss enhancer elements/DNA methylation-based, RNAss RNA expression-based, EREG-METHss epigenetically regulated DNA methylation-based, EREG-METHss epigenetically regulated RNA methylation-based

Fig.4

Mutation landscape of TGM1. A mutation landscapes of TGM1 for pan-cancer; B the top 5 mutation genes between high and low-expression of TGM1 in ACC patients; C the top 5 mutation genes between high and low-expression of TGM1 in BLCA patients; D the top 5 mutation genes between high and low-expression of TGM1 in KIRC patients; E the top 5 mutation genes between high and low-expression of TGM1 in LIHC patients

Fig.5

The Spearman analysis of TGM1 expression and regulatory genes and immune checkpoints. A the correlation of TGM1 expression with immune regulatory genes; B the correlation of TGM1 expression with immune checkpoint genes

Fig.6

The Spearman analysis of TGM1 expression and RNA modification; Tumor immune environment and its correlation with TGM1 expression and drug sensitivity analysis. A the correlation of TGM1 expression with genes of RNA modification; B the correlation of TGM1 expression with immune infiltrating cells using TIMER; C the correlation between gene expression and the sensitivity of GDSC drugs (top 10) in pan-cancer; D the correlation between gene expression and the sensitivity of CTRP drugs (top 10) in pan-cancer

Fig.7

The correlation of TGM1 expression with stromal score